Boolean Modeling of Biological Network Applied to Protein-Protein Interaction Network of Autism Patients

Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks' pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman's 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein-protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients' genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals.

Comparison of the central human and mouse platelet signaling cascade by systems biological analysis

Background

Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC).

Results

The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12.

Conclusions

Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07215-4.

Mathematical Techniques for Understanding Platelet Regulation and the Development of New Pharmacological Approaches

Mathematical and computational modeling is currently in the process of becoming an accepted tool in the arsenal of methods utilized for the investigation of complex biological systems. For some problems in the field, like cellular metabolic regulation, neural impulse propagation, or cell cycle, progress is already unthinkable without use of such methods. Mathematical models of platelet signaling, function, and metabolism during the last years have not only been steadily increasing in their number, but have also been providing more in-depth insights, generating hypotheses, and allowing predictions to be made leading to new experimental designs and data. Here we describe the basic approaches to platelet mathematical model development and validation, highlighting the challenges involved. We then review the current theoretical models in the literature and how these are being utilized to increase our understanding of these complex cells.

Analyzing ERK 1/2 signalling and targets

The ERK cascade (e.g. Raf-1) protects the heart from cell death and ischemic injury but can also turn maladaptive. Furthermore, an additional autophosphorylation of ERK2 at Thr188 (Erk1 at Thr208) allows ERK to phosphorylate nuclear targets involved in hypertrophy, stressing this additional phosphorylation as a promising pharmacological target. An in silico model was assembled and setup to reproduce different phosphorylation states of ERK 1/2 and various types of stimuli (hypertrophic versus non-hypertrophic). Synergistic and antagonistic receptor stimuli can be predicted in a semi-quantitative model, simulated time courses were experimentally validated. Furthermore, we detected new targets of ERK 1/2, which possibly contribute to the development of pathological hypertrophy. In addition we modeled further interaction partners involved in the protective and maladaptive cascade. Experimental validation included different gene expression data sets supporting key components and novel interaction partners as well as time courses in chronic heart failure.

Systems Analysis of Thrombus Formation

The systems analysis of thrombosis seeks to quantitatively predict blood function in a given vascular wall and hemodynamic context. Relevant to both venous and arterial thrombosis, a Blood Systems Biology approach should provide metrics for rate and molecular mechanisms of clot growth, thrombotic risk, pharmacological response, and utility of new therapeutic targets. As a rapidly created multicellular aggregate with a polymerized fibrin matrix, blood clots result from hundreds of unique reactions within and around platelets propagating in space and time under hemodynamic conditions. Coronary artery thrombosis is dominated by atherosclerotic plaque rupture, complex pulsatile flows through stenotic regions producing high wall shear stresses, and plaque-derived tissue factor driving thrombin production. In contrast, venous thrombosis is dominated by stasis or depressed flows, endothelial inflammation, white blood cell-derived tissue factor, and ample red blood cell incorporation. By imaging vessels, patient-specific assessment using computational fluid dynamics provides an estimate of local hemodynamics and fractional flow reserve. High-dimensional ex vivo phenotyping of platelet and coagulation can now power multiscale computer simulations at the subcellular to cellular to whole vessel scale of heart attacks or strokes. In addition, an integrated systems biology approach can rank safety and efficacy metrics of various pharmacological interventions or clinical trial designs.

Psoralen and Ultraviolet A Light Treatment Directly Affects Phosphatidylinositol 3-Kinase Signal Transduction by Altering Plasma Membrane Packing

Psoralen and ultraviolet A light (PUVA) are used to kill pathogens in blood products and as a treatment of aberrant cell proliferation in dermatitis, cutaneous T-cell lymphoma, and graft-versus-host disease. DNA damage is well described, but the direct effects of PUVA on cell signal transduction are poorly understood. Because platelets are anucleate and contain archetypal signal transduction machinery, they are ideally suited to address this. Lipidomics on platelet membrane extracts showed that psoralen forms adducts with unsaturated carbon bonds of fatty acyls in all major phospholipid classes after PUVA. Such adducts increased lipid packing as measured by a blue shift of an environment-sensitive fluorescent probe in model liposomes. Furthermore, the interaction of these liposomes with lipid order-sensitive proteins like amphipathic lipid-packing sensor and α-synuclein was inhibited by PUVA. In platelets, PUVA caused poor membrane binding of Akt and Bruton's tyrosine kinase effectors following activation of the collagen glycoprotein VI and thrombin protease-activated receptor (PAR) 1. This resulted in defective Akt phosphorylation despite unaltered phosphatidylinositol 3,4,5-trisphosphate levels. Downstream integrin activation was furthermore affected similarly by PUVA following PAR1 (effective half-maximal concentration (EC₅₀), 8.4 ± 1.1 versus 4.3 ± 1.1 μm) and glycoprotein VI (EC₅₀, 1.61 ± 0.85 versus 0.26 ± 0.21 μg/ml) but not PAR4 (EC₅₀, 50 ± 1 versus 58 ± 1 μm) signal transduction. Our findings were confirmed in T-cells from graft-versus-host disease patients treated with extracorporeal photopheresis, a form of systemic PUVA. In conclusion, PUVA increases the order of lipid phases by covalent modification of phospholipids, thereby inhibiting membrane recruitment of effector kinases.

Boolean modeling techniques for protein co-expression networks in systems medicine

INTRODUCTION

Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type.

AREAS COVERED

This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It's also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed. Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).

A comparative analysis of the bistability switch for platelet aggregation by logic ODE based dynamical modeling

A kinetic description of the fragile equilibrium in thrombozytes regulating blood flow would be an important basis for rational medical interventions. Challenges for such a model include regulation by a complex bistability switch that determines the transition from reversible to irreversible aggregation and sparse data on the kinetics. A so far scarcely applied technique is given by the derivation of ordinary differential equations from Boolean expressions, which are called logic ODEs. We employ a combination of light-scattering based thrombocyte aggregation data, western blot and calcium measurements to compare three different ODE approaches regarding their suitability to achieve a data-consistent model of the switch. Our analysis reveals the standardized qualitative dynamical system approach (SQUAD) to be a better choice than classical mass action formalisms. Furthermore, we analyze the dynamical properties of the platelet aggregation threshold as a basis for medical interventions such as novel platelet aggregation inhibitors.

Reciprocal regulation of human platelet function by endogenous prostanoids and through multiple prostanoid receptors

Platelets are permanently exposed to a variety of prostanoids formed by blood cells or the vessel wall. The two major prostanoids, prostacyclin and thromboxane act through well established pathways mediated by their respective G-protein coupled receptors inhibiting or promoting platelet aggregation accordingly. Yet the role of other prostanoids and prostanoid receptors for platelet function regulation has not been thoroughly investigated. We aimed at a comprehensive analysis of prostanoid effects on platelets, the receptors and pathways involved and functional consequences. We analyzed cAMP formation and phosphorylation of proteins pivotal to platelet function as well as functional platelet responses such as secretion, aggregation and phosphorylation. The types of prostanoid receptors contributing and their individual share in signaling pathways were analyzed and indicated a major role for prostanoid IP1 and DP1 receptors followed by prostanoid EP4 and EP3 receptors while prostanoid EP2 receptors appear less relevant. We could show for the first time the reciprocal action of the endogenous prostaglandin PGE2 on platelets by functional responses and phosphorylation events. PGE2 evokes stimulatory as well as inhibitory effects in a concentration dependent manner in platelets via prostanoid EP3 or EP4 and prostanoid DP1 receptors. A mathematical model integrating the pathway components was established which successfully reproduces the observed platelet responses. Additionally we could show that human platelets themselves produce sufficient PGE2 to act in an autocrine or paracrine fashion. These mechanisms may provide a fine tuning of platelet responses in the circulating blood by either promoting or limiting endogenous platelet activation.

What can proteomics tell us about platelets?

More than 130 years ago, it was recognized that platelets are key mediators of hemostasis. Nowadays, it is established that platelets participate in additional physiological processes and contribute to the genesis and progression of cardiovascular diseases. Recent data indicate that the platelet proteome, defined as the complete set of expressed proteins, comprises >5000 proteins and is highly similar between different healthy individuals. Owing to their anucleate nature, platelets have limited protein synthesis. By implication, in patients experiencing platelet disorders, platelet (dys)function is almost completely attributable to alterations in protein expression and dynamic differences in post-translational modifications. Modern platelet proteomics approaches can reveal (1) quantitative changes in the abundance of thousands of proteins, (2) post-translational modifications, (3) protein-protein interactions, and (4) protein localization, while requiring only small blood donations in the range of a few milliliters. Consequently, platelet proteomics will represent an invaluable tool for characterizing the fundamental processes that affect platelet homeostasis and thus determine the roles of platelets in health and disease. In this article we provide a critical overview on the achievements, the current possibilities, and the future perspectives of platelet proteomics to study patients experiencing cardiovascular, inflammatory, and bleeding disorders.

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model

For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre‐requisites are tumor models with multiple read‐out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three‐dimensional (3D) lung carcinoma models on a decellularized tissue matrix, providing a complex microenvironment for cell growth. For model generation, we used two cell lines with (HCC827) or without (A549) an activating mutation of the epidermal growth factor receptor (EGFR), exhibiting different sensitivities to the EGFR inhibitor gefitinib. EGFR activation in HCC827 was inhibited by gefitinib, resulting in a significant reduction of proliferation (Ki‐67 proliferation index) and in the induction of apoptosis (TUNEL staining, M30‐ELISA). No significant effect was observed in conventional cell culture. Results from the 3D model correlated with the results of an in silico model that integrates the EGFR signaling network according to clinical data. The application of TGFβ1 induced tumor cell invasion, accompanied by epithelial–mesenchymal transition (EMT) both in vitro and in silico. This was confirmed in the 3D model by acquisition of mesenchymal cell morphology and modified expression of fibronectin, E‐cadherin, β‐catenin and mucin‐1. Quantitative read‐outs for proliferation, apoptosis and invasion were established in the complex 3D tumor model. The combined in vitro and in silico model represents a powerful tool for systems analysis.

Combination of a human 3D lung tumor tissue model with a Boolean in silico Model.

Establishment of in silico signaling network topology for personalized medicine.

Significant decrease of tumor proliferation and induction of apoptosis upon in vitro treatment with tyrosine kinase inhibitors.

Decreased proliferation of tumor cells in the 3D model compared to 2D conditions.

Induction of invasion with EMT by TGFβ stimulation.